1 /**
2 * Copyright (c) 2011, The University of Southampton and the individual contributors.
3 * All rights reserved.
4 *
5 * Redistribution and use in source and binary forms, with or without modification,
6 * are permitted provided that the following conditions are met:
7 *
8 * * Redistributions of source code must retain the above copyright notice,
9 * this list of conditions and the following disclaimer.
10 *
11 * * Redistributions in binary form must reproduce the above copyright notice,
12 * this list of conditions and the following disclaimer in the documentation
13 * and/or other materials provided with the distribution.
14 *
15 * * Neither the name of the University of Southampton nor the names of its
16 * contributors may be used to endorse or promote products derived from this
17 * software without specific prior written permission.
18 *
19 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
20 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
21 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
22 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
23 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
24 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
25 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
26 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
27 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
28 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
29 */
30 package org.openimaj.image.model.patch;
31
32 import java.util.List;
33
34 import org.openimaj.image.FImage;
35 import org.openimaj.image.Image;
36 import org.openimaj.image.model.ImageClassificationModel;
37 import org.openimaj.util.pair.IndependentPair;
38
39 /**
40 * An {@link ImageClassificationModel} based on the idea of determining the
41 * probability of a class of a pixel given the local patch of pixels surrounding
42 * the pixel in question. A sliding window of a given size is moved across the
43 * image (with overlap), and the contents of the window are analysed to
44 * determine the probability belonging to the pixel at the centre of the window.
45 *
46 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
47 *
48 * @param <Q>
49 * Type of pixel
50 * @param <T>
51 * Type of {@link Image}
52 */
53 public abstract class PatchClassificationModel<Q, T extends Image<Q, T>> implements ImageClassificationModel<T> {
54 private static final long serialVersionUID = 1L;
55
56 protected int patchHeight, patchWidth;
57
58 /**
59 * Construct with the given dimensions for the sampling patch.
60 *
61 * @param patchWidth
62 * the width of the sampling patch
63 * @param patchHeight
64 * the height of the sampling patch
65 */
66 public PatchClassificationModel(int patchWidth, int patchHeight) {
67 this.patchHeight = patchHeight;
68 this.patchWidth = patchWidth;
69 }
70
71 /**
72 * Classify a patch, returning the probability of the central pixel
73 * belonging to the class.
74 *
75 * @param patch
76 * the patch.
77 * @return the probability of the central pixel belonging to the class.
78 */
79 public abstract float classifyPatch(T patch);
80
81 @Override
82 public FImage classifyImage(T im) {
83 final FImage out = new FImage(im.getWidth(), im.getHeight());
84 final T roi = im.newInstance(patchWidth, patchHeight);
85
86 final int hh = patchHeight / 2;
87 final int hw = patchWidth / 2;
88
89 for (int y = hh; y < im.getHeight() - (patchHeight - hh); y++) {
90 for (int x = hw; x < im.getWidth() - (patchWidth - hw); x++) {
91 im.extractROI(x - hw, y - hh, roi);
92 out.pixels[y][x] = this.classifyPatch(roi);
93 }
94 }
95
96 return out;
97 }
98
99 @Override
100 public abstract PatchClassificationModel<Q, T> clone();
101
102 protected abstract T[] getArray(int length);
103
104 @Override
105 public boolean estimate(List<? extends IndependentPair<T, FImage>> data) {
106 final T[] samples = getArray(data.size());
107 for (int i = 0; i < data.size(); i++) {
108 samples[i] = data.get(i).firstObject();
109 }
110 learnModel(samples);
111
112 return true;
113 }
114
115 @Override
116 public int numItemsToEstimate() {
117 return 1; // need a minimum of 1 sample
118 }
119
120 @Override
121 public FImage predict(T data) {
122 return classifyImage(data);
123 }
124 }